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Development and public release of the Penn Reading Assessment Computerized Adaptive Test (PRA-CAT) for premorbid IQ.

Mikhal A YudienTyler Maxwell MooreAllison M PortKosha RuparelRaquel E GurRuben C Gur
Published in: Psychological assessment (2019)
An important component of neuropsychological testing is assessment of premorbid intelligence to estimate a patient's ability independent of neurological impairment. A common test of premorbid IQ-namely, the Reading section of the Wide Range Achievement Test (WRAT)-has been shown to have high measurement error in the high ability range, is unnecessarily long (55 items), and is proprietary. We describe the development of an alternative, nonproprietary, computerized adaptive test for premorbid IQ, the Penn Reading Assessment (PRA-CAT). PRA-CAT items were calibrated using a 1-parameter item response theory model in a large community sample (N = 9,498), Ages 8 to 21, and the resulting parameters were used to simulate computerized adaptive testing sessions. Simulations demonstrated that the PRA-CAT achieves low measurement error (0.25; equivalent to Cronbach's alpha = .94) and acceptable measurement error (0.40; Cronbach's alpha = .84) after only 18 and 6 items, respectively (on average). Correlation of WRAT and PRA-CAT scores with numerous clinical, cognitive, demographic, and neuroimaging criteria suggests that validity of PRA-CAT score interpretation is comparable (and sometimes superior) with the WRAT. The fully functioning PRA-CAT for public use (including item parameter estimates reported here) has been built using the open-source program Concerto, and can be installed by anyone on a local computer or on the "cloud." Given the length and proprietary nature of the WRAT, the PRA-CAT shows promise as a potential alternative (and with minimal or no cost). Further validation in the context of neurological injury is needed. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
Keyphrases
  • healthcare
  • working memory
  • clinical decision support
  • machine learning
  • case report
  • brain injury
  • big data
  • adverse drug